Delay-Tolerant Distributed-TAST Codes for Cooperative Diversity
Bibliographic record
Abstract
In cooperative networks using a decode-and-forward strategy, the multiple relays effectively transmit a distributed space-time code, the performance of which can be severely degraded when timing synchronization among the relays is not assured (e.g., in cases of broadcast to dispersed recipients or in networks without a shared, high-quality timing reference). Recent work by Xia and Hammons have investigated the design of distributed space-time codes that are delay tolerant, in the sense that full spatial diversity is achieved regardless of timing offsets. In general, the previously known space-time block codes belonging to the class of C-linear codes, however, which are important because they achieve full spatial diversity and admit near-optimal lattice decoding algorithms, are not delay tolerant. In this paper, we present a new family of such codes that are fully delay tolerant. The new codes generalize the threaded algebraic space-time (TAST) codes introduced by El Gamal and Damen. Like their brethren, the new distributed-TAST codes are effective and flexible, enabling use of different signaling constellations, transmission rates, numbers of transmit and receive antennas, and decoders of varying levels of complexity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".